Petroleum exploration and prediction apparatus and method

ABSTRACT

A method for predicting the state of a geological formation. The method may include generating a separation key effective to extract a first feature from a signal or signals corresponding to a first state and a second feature, distinct from the first feature, from a signal or signals corresponding to a second state. The separation key may list at least one feature operator and a weighting table. The at least one feature operator may expand a test signal collected from a geological formation of unknown state in at least one of frequency space and time space to generate a plurality of feature segments. A weighting table may weight the plurality of feature segments. The weighted plurality of feature segments may be superimposed to form a third feature. The geological formation may be classified as having one of the first state and second state based on the correspondence of the third feature to one of the first feature and second feature.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser.No. 60/416,342, filed Oct. 4, 2002, and entitled SEISMIC EVENT CONTRASTSTACKING AND OTHER USES OF EVENT RESOLUTION IMAGING WITHIN THE OIL ANDGAS INDUSTRY.

BACKGROUND

1. The Field of the Invention

This invention relates to signal processing and, more particularly, tonovel systems and methods for pattern recognition and datainterpretation relative to monitoring and categorizing patterns forpredictably detecting and quantifying hydrocarbon deposits.

2. The Background Art

Seismic waves have been used to generate models of the earth'scomposition. In more recent times, seismic waves have been employed inan effort to located resources such as oil and gas deposits within theearth's surface formations. Well log data has also been applied topredicting resources within the earth. However, because of the lowsignal-to-noise ration (SNR) or high noise-to-signal ratio andcomplexity of seismic waves and well log data, generating accuratemodels of resource deposits has been difficult.

To facilitate the extraction of useful information, various types ofsignal processing strategies have been applied to seismic waves and welllog data. Analysis strategies used by those skilled in the art haveincluded spectral analysis, seismic trace stacking, various transforms,time-frequency distributions, spatial filtering methods, neuralnetworks, fuzzy logic systems, and integrated neurofuzzy systems. Asappreciated, each of these analysis techniques, however, typicallyrelies on human inspection of the generated waveforms. Visual inspectionmay miss vital content that is implicit or hidden (e.g. time domaininformation).

Stacking of multiple seismic traces from pre-stack gathers generallyemploys summing or averaging signals acquired over many angles ofincidence and many offsets. The end goal of stacking is to reduce noiseand amplify certain, useful, seismic, waveforms. However, it furtherobscures other data. While useful for certain applications, averagingand stacking techniques have several significant drawbacks. Largequantities of information, just as valuable but less understood, arelost in the averaging or stacking. Only selected types of signals areable to survive massive averaging or summation over multiple offsets.Moreover, the averaging process only provides a comparison betweengroups of offsets or groups of angles rather than between the individualoffsets or angles.

Alternative analysis approaches including Fourier Transforms; HilbertTransforms; Wavelet Transforms; Short-Time Fourier Transforms; WignerFunctions; Generalized Time-Frequency Distributions; Parameter vs.Offset (PVO); and Amplitude vs. Offset (AVO) have been applied toseismic waves and well log data. While valuable for certainapplications, these approaches typically require averaging over smallgroups of angles or small groups of offsets. Moreover, these approacheshave not been fully integrated with computerized conditiondiscrimination. Like spectral analysis techniques, these approaches relyon visual inspection of the generated waveforms, greatly increasing thepossibility of error.

Spatial filtering methods, including: Principal Component Analysis;Singular Value Decomposition; and Eigenvalue Analysis have been appliedto seismic waves and well log data. Such filtering methods tend toignore frequency and temporal information. Additionally, these filteringtechniques are usually applied only to seismic traces that have beenaveraged (post-stack seismic traces), otherwise the noise level isprohibitive.

Additional analysis techniques and methodology have been developed bythose skilled in the art, to take advantage of recent increases incomputer processing power. Neural networks have been developed todiscover discriminate information. The traditional neural networkapproaches, however, generally take a long time to program and learn,are difficult to train, and tend to focus on local minima to thedetriment of other more global and important areas. Moreover, most ofthese analysis techniques are limited by a lack of integration withtime, frequency, and spatial analysis techniques.

Due to their inherent narrow ranges of applicability, prior methods ofanalysis have provided a fragmentary approach to seismic waves and welllog data analysis. What is needed is an integrated waveform analysismethod capable of extracting useful information from highly complex andirregular waveforms such as raw seismic data, pre-stack seismic gathers,post-stack seismic traces, and the variety of signal types comprisingwell log data sets.

BRIEF SUMMARY OF THE INVENTION

In accordance with the invention as embodied and broadly describedherein, apparatus and methods in accordance with the present inventionmay include an event contrast stacker arranged to process seismictraces, well log data, and the like to produce reliable and accurateinformation about a geological formation. Particularly, characteristicsignals relating to a geological formation may be gathered, amplified,processed, and recorded. Such signals may include seismic traces (rawtraces, pre-stack gathers, post-stack gathers, and the like), well logdata, and any other waveform or measured value believed to containinformation as to the content, state, or composition of the geologicalformation.

The strategy of an event contrast stacker in accordance with the presentinvention is to apply several methods of analysis to each epoch (timeperiod of interest) to find and exhibit consistent differences betweenepochs relating to different states and similarities between epochsrelated to similar states. An event contrast stacker may include asignal pre-processor, a learning system, a classification system, and anoutput generator.

A signal pre-processor may provide any filtering, amplification, and thelike that may prepare the signal for further processing. Additionally,the signal pre-processor may divide the signal into time segments orepochs. Each epoch may be labeled according to the state, if known, ofthe geological formation from which the data pertaining to an epoch wascollected.

A collection of data from epochs, where the physical system representedthereby is of a known state may be passed to a learning system. Thelearning system may use several waveform analysis techniques including,by way of example and not limitation, time-frequency expansion, featurecoherence analysis, principal component analysis, and separationanalysis. For convenience we may refer to any set of data recorded overan epoch of time and relating to the same sensed system as an “epoch,”even though an epoch is literally just the application time segment.Each epoch may be expanded by feature operators (mathematicalmanipulations applying waveform analysis techniques) to generate featuresegments in an extended phase space representing spatial, time,frequency, phase, and interchannel relationships. The various featuresegments corresponding to an epoch may be weighted in an effort tolocate the feature segments containing information correspondingexclusively to the state of the epoch. Weightings or weights may bethough of as respective coefficients for each mathematical functioncontributing to composite or sum of contributing functions. Thus aweight is a proportion of contribution of a function or value.

Once weighted, the feature segments corresponding to an epoch may besummed or superimposed. If successful, the superposition provides aresulting waveform containing a non-random feature or pattern uniquelycorresponding to the state of the epoch. If unsuccessful, the operationprovides no distinction and the learning system may begin anotheriteration and apply a different combination of feature operators,feature weights, or both feature operators and feature weights. Thelearning system may continue processing until the superimposed featuresegments of an epoch result in a characteristic feature correspondingexclusively to the state of the epoch. Once the effective featureoperators, feature weights, and the like have been determined, they maybe incorporated into a separation key. A separation key provides featureoperators and weights, along with a resulting waveshape or othercharacteristic that will reliably distinguish two opposing states.

In one embodiment of a system in accordance with the present invention,a classification system may use the separation key and apply the featureoperators and weights (previously determined to be optimal) to aselected group of epochs referred to as classification epochs.Classification epochs may have known states or unknown states. Trueepoch state labels may be bound to analyzed epochs to enable acomparison with epoch classifications generated by the classificationsystem. That is, the actual or true state associated with a particularepoch may provide a key to determine if that epoch has been correctlyclassified. Accordingly, this may provide a method of testing orvalidating the accuracy of an event contrast stacker. Highclassification accuracy of non-training epochs (separate and distinctfrom the learning epochs used in the creation of the separation key),indicates a valid, derived, separation key capable of repeatedlyseparating signals according to the state of the geological formationfrom which the signals were collected.

The classification system may forward certain data to an outputgenerator to compile a statistical summary of the results. Additionaloutputs may include calculations of sensitivity, specificity and overallaccuracy.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing features of the present invention will become more fullyapparent from the following description and appended claims, taken inconjunction with the accompanying drawings. Understanding that thesedrawings depict only typical embodiments of apparatus and methods inaccordance with the invention and are, therefore, not to be consideredlimiting of its scope, the invention will be described with additionalspecificity and detail through use of the accompanying drawings inwhich:

FIG. 1 is a schematic diagram of a geological formation having primaryseismic waves propagated therethrough and reflected seismic wavesrecorded therefrom;

FIG. 2 is a schematic diagram of a well undergoing well log datacollection;

FIG. 3 is a schematic block diagram illustrating a signal migrator inaccordance with the present invention;

FIG. 4 is a perspective view of a three dimensional seismic volumecomprising a collection of seismic traces;

FIG. 5 is a schematic block diagram of an embodiment of an eventcontrast stacker in accordance with the present invention;

FIG. 6 is a schematic block diagram of an embodiment of a learningsystem from an event contrast stacker in accordance with the presentinvention;

FIG. 7 is a graph of a feature operator comprising a weighting in a timespace (domain);

FIG. 8 is a graph of a feature operator comprising a weighting in afrequency space (domain);

FIG. 9 is a table illustrating a feature map in accordance with thepresent invention wherein the signal epoch has been expanded into threetime segments and twelve frequency segments to generate a total ofthirty-six feature segments;

FIG. 10 is a table illustrating an embodiment of a weighting table haveweights to be applied to the feature segments of FIG. 9 in accordancewith the present invention;

FIG. 11 is a schematic block diagram illustrating an example epochcorresponding to a state A before and after processing by an eventcontrast stacker in accordance with the present invention;

FIG. 12 is a schematic block diagram illustrating an example epochcorresponding to a state B before and after processing by an eventcontrast stacker in accordance with the present invention;

FIG. 13 is a table illustrating an embodiment of a separation keygenerated by a learning system in accordance with the present invention;

FIG. 14 is a graph of the separation key of FIG. 13;

FIG. 15 is a schematic block diagram of an embodiment of aclassification system from an event contrast stacker in accordance withthe present invention;

FIG. 16 is an alternative embodiment of an event contrast stacker inaccordance with the present invention;

FIG. 17 is a schematic block diagram of an alternative embodiment of aclassification system from an event contrast stacker in accordance withthe present invention;

FIG. 18 is a schematic block diagram of an embodiment of an outputgenerator from an event contrast stacker in accordance with the presentinvention;

FIG. 19 is a graph of an activation value plot generated by an outputgenerator in accordance with the present invention;

FIG. 20 is a graph of an alternative activation value plot generated byan output generator in accordance with the present invention;

FIG. 21 is a schematic block diagram illustrating the formation of areliability matrix by an output generator in accordance with the presentinvention;

FIG. 22 is a schematic block diagram illustrating the formation of adiscrimination matrix by an output generator in accordance with thepresent invention;

FIG. 23 is a schematic block diagram illustrating the formation of adissimilarity matrix by an output generator in accordance with thepresent invention;

FIG. 24 is a schematic block diagram illustrating the formation of asimilarity matrix by an output generator in accordance with the presentinvention;

FIG. 25 is a schematic block diagram illustrating an alternativeembodiment of a similarity matrix in accordance with the presentinvention;

FIG. 26 is a schematic block diagram illustrating the formation of acontrast stacked signal by an event contrast stacker in accordance withthe present invention;

FIG. 27 is a schematic diagram of a seismic contrast volume generated byan event contrast stacker in accordance with the present invention;

FIG. 28 is a schematic diagram of a three-dimensional imagecorresponding to the seismic contrast volume of FIG. 27 in accordancewith the present invention;

FIG. 29 is a schematic diagram of a two-dimensional, horizontal imagecorresponding to the seismic contrast volume of FIG. 27 in accordancewith the present invention;

FIG. 30 is a schematic diagram of a two-dimensional vertical imagecorresponding to the seismic contrast volume of FIG. 27 in accordancewith the present invention;

FIG. 31 is a schematic diagram of a number plot corresponding to theseismic contrast volume of FIG. 27 in accordance with the presentinvention;

FIG. 32 is a schematic diagram of a color plot corresponding to theseismic contrast volume of FIG. 27 in accordance with the presentinvention;

FIG. 33 is a schematic diagram of selected seismic traces containing acommon event;

FIG. 34 is a schematic diagram of the selected seismic traces of FIG. 33migrated in accordance with the present invention to align commonevents;

FIG. 35 is a schematic diagram of selected seismic traces containingvarious events;

FIG. 36 is a schematic diagram of the selected seismic traces of FIG. 35migrated in accordance with the present invention to align the variousevents;

FIG. 37 is a two-dimensional, horizontal image derived, using prior artmethods, from actual seismic data collected from an oil field;

FIG. 38 is a two-dimensional, horizontal image derived from datagenerated by an event contrast stacker in accordance with the presentinvention from seismic data collected from the oil field;

FIG. 39 is a two-dimensional, vertical image derived, using prior artmethods, from actual seismic data from the oil field;

FIG. 40 is a two-dimensional, vertical image derived from data generatedby an event contrast stacker in accordance with the present inventionfrom seismic data collected from the oil field;

FIG. 41 is a table illustrating the status, time window examined, andnumber of traces processed in accordance with the present invention foreach of the various wells drilled in the oil field;

FIG. 42 is a table illustrating a portion of a separation key found byan event contrast stacker in accordance with the present invention to beeffective on seismic data collected from the oil field;

FIG. 43 is a graph of an activation value plot generated by an eventcontrast stacker in accordance with the present invention from seismicdata collected from the oil field;

FIG. 44 is a table illustrating the status, time window examined, andnumber of traces processed in accordance with the present invention foreach of the various wells drilled in a gas field;

FIG. 45 is a table illustrating a portion of one embodiment of aseparation key found by an event contrast stacker, in accordance withthe present invention, to be effective over an 80 millisecond window onseismic data collected from the gas field;

FIG. 46 is a table illustrating a portion of an alternative embodimentof a separation key found by an event contrast stacker in accordancewith the present invention to be effective over a 200 millisecond windowon seismic data collected from the gas field;

FIG. 47 is a two-dimensional, vertical image derived, using prior artmethods, from actual seismic data collected from the gas field;

FIG. 48 is a two-dimensional, vertical image derived from data generatedby an event contrast stacker in accordance with the present inventionover an 80 millisecond window of seismic data collected from the gasfield;

FIG. 49 is a two-dimensional, vertical image derived from data generatedby an event contrast stacker in accordance with the present inventionover a 200 millisecond window of seismic data collected from the gasfield;

FIG. 50 is a table illustrating a portion of a separation key found byan event contrast stacker in accordance with the present invention to beeffective in segregating seismic data pertaining to gas production abovea selected economic value from seismic data pertaining to gas productionbelow a selected economic value; and

FIG. 51 is a table illustrating a portion of a separation key found byan event contrast stacker in accordance with the present invention to beeffective in segregating seismic data pertaining to hydrocarbon depositsfrom seismic data pertaining to water deposits.

DETAILED DESCRIPTION

It will be readily understood that the components of the presentinvention, as generally described and illustrated in the Figures herein,could be arranged and designed in a wide variety of differentconfigurations. Thus, the following more detailed description of theembodiments of the system and methods in accordance with the presentinvention, as represented by FIGS. 1 through 51, is not intended tolimit the scope of the invention, as claimed, but is merelyrepresentative of the presently preferred embodiments of the invention.

Certain embodiments of apparatus and methods in accordance with thepresent invention incorporate the hardware and software of the signalinterpretation engine disclosed in U.S. Pat. No. 6,546,378, filed Apr.24, 1997, and entitled SIGNAL INTERPRETATION ENGINE, incorporated hereinby reference. The present application does not attempt to describe everydetail of the signal interpretation engine. To this end, the details ofthe signal interpretation engine are contained in the patentspecification directed thereto. Whereas, only a general description ofselected modules and procedures is presented herewith.

Referring to FIG. 1, in general, a seismic study 10 may be conducted bypositioning at least one source 12 and at least one receiver 14 on,above, or within the earth's surface 16. A source 12 may generate aprimary seismic wave 18 in a selected geological area 19 or geologicalformation 19. As a primary wave 18 travels though the geologicalformation 19, it may encounter reflectors 20. Reflectors 20 may bechanges in the earth's make-up, striations, strata, differentials indensity, differentials in stiffness, differentials in elasticity,differentials in porosity, changes in phase, and the like. Variousreflectors 20 a, 20 b, 20 c may reflect the primary wave 18 creatingcorresponding reflected seismic waves 22 a, 22 b, 22 c. The reflectedwaves 22 may be recorded, in the order of their arrival, by a receiver14. Reflected waves 22 gathered by a receiver 14 may be used tointerpret the composition, fluid content, extent, geometry, and the likeof geological formations 19 far below the earth's surface 16.

In general, sources 12 may be selected from any devices for generating aseismic wave 20. Suitable sources 12 may include air guns, explosivecharges, vibrators, vibroseis trucks, and the like. A receiver 14 may beany device that detects seismic energy in the form of ground motion (ora pressure wave in fluid) and transforms it to an electrical impulse 24or signal 24. Generally, receivers 14 are referred to as geophones, foruse on land, and hydrophones, for use on water. The electrical impulse24 recorded by a receiver 14 may be referred to as a seismic trace 24. Atrace 24 may, therefore, be defined as a recording of the response ofthe earth 19 to seismic energy passing from a source 12, throughsubsurface layers (reflectors 20), and back to a receiver 14.

The seismic waves 18, 22 produced by a source 12 and recorded by areceiver 14 are generally in the frequency range of approximately 1 to120 Hz. Seismic waves 18, 22 may be divided into two main categories,namely, pressure waves and shear waves. Pressure waves are elastic bodywaves or sound waves in which particles oscillate in the direction thewave propagates. Shear waves are elastic body waves in which particlesoscillate perpendicular to the direction in which the wave propagates.Shear waves may be generated when pressure waves impinge on an interfaceat non-normal incidence. Shear waves can likewise be converted topressure waves.

Referring to FIG. 2, in certain applications, well log data 26 may beused to provide additional information about selected geologicalformations 19 below the earth's surface 16. Well log data 26 may becollected by lowering an instrument array 28 into a well bore 30. Theinstrument array 28 may measure or record any characteristic of the wellenvironment 32. For example, an instrument array 28 may emit variouswaves into the well environment 32 and record the response.Additionally, an instrument array 28 may measure temperature, pressure,conductivity, and the like of the well environment 32. Well log data 26may be used to better understand geological formations 19 penetrated bywells 30.

Referring to FIG. 3, in certain embodiments of apparatus and methods inaccordance with the present invention, a geologic study 10 may collect afirst data bundle 34, corresponding to a first geological formation 19having state A. In a similar manner, a geological study 10 may collect asecond data bundle 36, corresponding to a second geological formation 19having state B. The data bundles 34, 36 may contain seismic traces 24,well log data 26, some other measured signal, value, or the like, or anycombination thereof.

Hereinafter, data processed in accordance with the present invention maybe referred to generically as a signal 24. However, it should berecognized that a signal 24 may include seismic traces 24 (e.g. rawtraces, pre-stack gathers, post-stack gathers, attribute volumes, andthe like), well log data 26, and any other waveform or measured valuecontaining information as to the content, state, or composition of ageological formation 19.

In embodiments utilizing seismic traces 24, the data bundles 34, 36 maybe processed by a signal migrator 38. In general, a signal migrator 38may process traces 24 by applying filtering 40, a pre-stack migration42, stacking 44, a post-stack migration 46, or any combination thereof.After processing of signals 24 by the signal migrator 38, the signalmigrator 24 may provide a first record 48 to store data corresponding tothe first data bundle 34. A second record 50 may be generated to storedata corresponding to the second data bundle 36.

A “migration” 42, 46 of a seismic trace 24 is a complex process todetermine the location in three-dimensional space from which the trace24 most likely originated. Migration 42, 46 often involves applying apredicted velocity profile for the geological area 19 being studied.That is, various materials transfer seismic waves 18, 22 at differentvelocities. By taking what is known about a particular geologicalformation 19, an estimate may be formulated for how long it would take aprimary seismic wave 18 to travel down to a particular reflector 20,reflect, and travel as a reflected seismic wave 22 back to the surface16. The longer the time for a reflected wave 22 to arrive at the surface16, the deeper the reflector 20 and origin of the trace 24 is likely tobe. This process may, however, be complicated by the ability of waves18, 22 to reflect back and forth between reflectors 20 before arrivingat the surface. Thus, the travel time of certain signals 24 may beartificially prolonged.

Using velocity profiles and various other techniques, geologists mayprovide an approximation of the location where a trace signal 24 wasgenerated in three-dimensional space. This locating process may beimportant because it ties the information contained within a trace 24,or a portion of a trace 24, to a particular location.

Stacking 44 is often simply the averaging or summing of a signal 24 withother signals 24 collected by the same receiver 14 or by other receivers14 in the same area. Stacking 44 is typically used in an attempt toamplify common characteristic of the signals 24. However, in certainapplications, stacking 44 by averaging or summing may destroy or canceluseful information.

The first and second records 48, 50 may be generated at various stagesduring processing by a signal migrator 38. For example, the first andsecond records 48, 50 may be generated upon completion of the pre-stackmigration 42, stacking 44, or the post-stack migration 46. Traces 24processed only through a pre-stack migration 42 may be referred to aspre-stack gathers. Pre-stack gathers may be rich with hiddeninformation. However, traces 24 processed through a post-stack migration46 (generally referred to as post-stack gathers) may also containsufficient informational content to be useful.

In general, data 34, 36 not in the form of a seismic trace 24 collectedfrom the surface 16 need not be processed by a signal migrator 38.Migration 42, 46, which is essentially an attempt to locate the sourceof signals 24 that have traveled large distances, is not necessary whenthe source of the data is already known. For example, well log data 26by definition is tied to the area surrounding a well 30, thus migration42, 46 may not be needed. It should be recognized, however, that data34, 36 in any form may be filtered, amplified, or otherwise processed asneeded before the first 48 and second records 50 are generated.

Referring to FIG. 4, first and second data bundles 34, 36 in accordancewith the present invention may represent any collection of information.In certain embodiments, a data bundle 34, 36 may comprise all, or anyportion, of a three-dimensional seismic volume 52. A three-dimensionalseismic volume 52 may be any mathematical space (domain), defined by anX-axis 54, Y-axis 56, and Z-axis 58, containing a selected number ofsignals 24 positioned therewithin. A three-dimensional seismic volume 52may be aligned so increasing time 60 of the recorded signals 24 isaligned with the Z-axis 58. Thus, progress in the negative directionalong the Z-axis 58 may indicate increasing time 60 as well asincreasing depth into the earth 19.

As stated hereinabove, a data bundle 34, 36 may comprise all, or anyportion, of a three-dimensional seismic volume 52. Thus, a data bundle34, 36 may represent a single signal 24, a portion of a single signal24, multiple signals 24, or portions of multiple signals 24. If portionsof multiple signals representing a particular value of time (depth) areutilized, the collection may be referred to as a horizon 62. A databundle 34, 36 comprising a horizon may be useful for extractinginformation about a particular “pay horizon” or other suspectedhydrocarbon deposit.

Referring to FIG. 5, once processed as desired, the first and secondrecords 48, 50 may be forwarded to a event contrast stacker 64. Incertain embodiments, an event contrast stacker 64 in accordance with thepresent invention applies several methods of analysis to the databundles 34, 36 to find consistent similarities within signals 24 relatedto similar states and differences between signals 24 relating todifferent states.

In certain embodiments, an event contrast stacker 64 may begin bypassing the first and second records 48, 50 through a signalpre-processor 66. The signal pre-processor 66 may divide the records 48,50 into epochs 68. An epoch 68 may be defined as a time segment of asignal 24. A label 70 may be applied to each epoch 68 to identify thestate of the geological formation 19 from which the epoch 68 wascollected. For example, epochs collected from a first geologicalformation 19 may have a label indicating that the epochs correspond to astate A. Similarly, epochs from a second geological formation 19 mayhave a label indicating that the epochs correspond to a state B, Not-Aand distinct from A.

The state of a geological formation 19 may be any characteristic of theformation 19 whose presence or absence may be worth predicting,quantifying, or the like. In general, state A may be the presence of acharacteristic, while state B is the absence of the characteristic.Thus, state B is typically the state Not-A. For example, state A may bethe presence of a hydrocarbon deposit, while state B is the absence of ahydrocarbon deposit. State A may be oil production above an thresholdvalue, while state B is oil production below a threshold value. Othersuitable state pairs include: presence of sand, absence of sand;presence of shale, absence of shale; density above a threshold value,density below a threshold value; water content above a threshold value,water content below a threshold value; porosity above a threshold value,porosity below a threshold value; gas production above a thresholdvalue, gas production below a threshold value; permeability above andbelow a threshold value; presence and absence of salt; presence andabsence of absorbed noncondensible gases (fizz water); presence andabsence of faults; or the like.

In selected embodiments, states A and B may be differentiatedeconomically. For example, state A may be hydrocarbon production over$1000 per day, while state B may be hydrocarbon production below $50 perday. In another embodiment, state A may be an economically viablehydrocarbon well (production sufficient to cover operating costs), whilestate B is a non-economically viable hydrocarbon well (productioninsufficient to cover operating costs). In short, states A and B may beany two determinable conditions, qualities, characteristics, productionrates, or the like of geological formations 19.

In certain embodiments, the labels 70 applied to the epochs 68 may alsocontain location information. For example, a label 70 may contain acoordinate (e.g. ordered triplet), or other designation, to identify thelocation of the epoch 68 in a three-dimensional or other seismic volume52.

Once segmented and labeled, a selected number of the epochs 68, eachknown to represent a known state A or state B, may be designated aslearning epochs 72. Similarly, a selected number of the epochs 68unknown as to their representing state A or state B may be designated asclassification epochs 74. The learning epochs 72 may be forwarded to alearning system 76 while the classification epochs 74 may be forwardedto a classification system 78.

In selected embodiments, the learning system 76 may operate on thelearning epochs 72 until a suitable interpretation map 80 or separationkey 80 is generated. A separation key 80 may be considered completewhen, upon application thereof to the learning epochs 72, a non-randompattern corresponding to one of state A or state B is generated. Afterformulation, the separation key 80 may be transmitted to theclassification system 78. In certain embodiments, the classificationsystem 78 may provide a test to verify the utility of the newlygenerated separation key 80. Additionally, the classification system 78may analyze and expand the classification epochs 74 in accordance withthe information supplied by the separation key 80.

At any time during processing, selected information may be exported fromthe learning system 76, the classification system 78, or both thelearning system 76 and the classifications system 78, to an outputgenerator 82 for conversion into a useful and easily accessible format.

In selected embodiments, an event contrast stacker 62 may beincorporated into a single unit incorporating both hardware and softwarein accordance with the present invention. In such a configuration, adrive, network connection, or the like may be provided for receiving thefirst and second records 48, 50. In an alternative embodiment, an eventcontrast stacker 64 may simply be a personal computer having anappropriate hardware and software configuration sufficient to provide adesired level of data reception, recordation, amplification, andmanipulation capabilities.

Those skilled in the art will readily recognize that various othermodules or systems may be incorporated in connection with an eventcontrast stacker 64 in accordance with the present invention. It isintended, therefore, that the examples provided herein be viewed asexemplary of the principles of the present invention, and not asrestrictive to particular structures, systems, modules, or methods forimplementing those principles.

Referring to FIGS. 6-13, the learning system 76 may receive and processlearning epochs 72 to compile an optimized formula (i.e. separation key80) for segregating epochs 72 by state. Within a learning system 76,learning epochs 72 may first be processed in a feature expansion module84. A feature expansion module 84 may provide 86 a collection of featureoperators 88 comprising various mathematical manipulations. Thecollection of feature operators 88 may be stored within the featureexpansion module 84 or input by a user. Additionally, a featureexpansion module 84 may also be arranged to store a collection offeature operators 88 as well as receive feature operators 88 input by auser.

By processing each epoch 72 through a multitude of feature operators 88,unique characteristics 90 or features 90 corresponding to a particularstate may be magnified to the point that they become easily discernableto a computerized criterion or to a discerning user. A feature 90 may beany non-random pattern corresponding exclusively to epochs 72 of aparticular state, as opposed to “not that particular state.” Whilecertain feature operators 88, or combinations of feature operators 88,may be effective to produce repeatable features 90 in epochs 72 of acommon state, other feature operators 88 may be ineffective. Byprocessing the epochs 72 with a collection of feature operators 88, themost effective feature operators 88 or combination of feature operators88 may be identified.

In selected embodiments, the feature expansion module 84 may processeach epoch 72 individually. In other embodiments, the feature expansionmodule 84 may consolidate epochs 72 before processing. For example, whenmultiple input signals 24 are contained within an epoch 72, the featureexpansion module 84 may superimpose any combination of the input signals24 to create a composite signal. Selected signals 24 of an epoch 72 maybe processed individually while others may be combined and analyzed insuperposition.

In certain embodiments, a feature expansion module 84 may process alearning epoch 72 with feature operators 88 utilizing multiple waveformanalysis techniques including time-frequency expansion, featurecoherence analysis, principal component analysis, separation analysis,or the like. For example, processing learning epochs 72 with featureoperators 88 may include applying frequency weighting factors, phaseweighing factors, amplitude weighting factors, selective superpositionof signals 24, or the like. In selected embodiments, processing learningepochs 72 with feature operators 88 may also include comparing spacialpattern, signal 24 shape, area under the curve of selected signals 24,or the like.

During processing by a feature operator 88, each learning epoch 72 maybe decomposed into feature segments 92 in an extended phase spacerepresenting space, time, frequency, phase, or the like. The featuresegments 92 pertaining to a selected epoch 72 may be collected togenerate 94 a feature map 96. For example, in the illustratedembodiments of FIGS. 7 and 8, two feature operators 88 may expand anepoch 72. The first feature operator 88 a may expand the epoch 72 intothree time segments 98. The second feature operator 88 b may expand eachtime segment 96 into twelve frequency bands 100.

The first and second operators 88 a, 88 b may expand the epoch 72 intotime segments 98 and frequency bands 100 by any suitable method. Forexample, in the illustrated embodiments of FIGS. 7 and 8, a Gaussianweighting 102 may be used to define the bounds of the time segments 98and frequency bands 100. The Gaussian weighting 102 a of the firstfeature operator 88 a may be defined in terms of a central time 104 anda time width 106. If desired, the time width 106 may represent thelocation where the weighting of the Gaussian distribution 102 a is halfthe maximum weighting. Similarly, the Gaussian weighting 102 b of thesecond feature operator 88 b may be defined in terms of a centralfrequency 108 and a frequency width 110. If desired, the frequency width110 may also represent the location where the weighting of the Gaussiandistribution 102 b is half the maximum weighting.

A feature map 96 may be generated 94 in any suitable manner. In theillustrated embodiment of FIG. 9, rows 112 may represent the variousfrequency bands 100 into which the epoch 72 was expanded. Columns 114may represent the various time segments 98 into which the epoch 72 wasexpanded. Thus, each feature segment 92 may be charted according to itscentral frequency 108 and central time 104.

Once completed, a feature map 96 may be forwarded to a weighting module116. Within a weighting module 116, a weight table 118 may be generated120. A weight table 118 and accompanying weights 122 may be based onsome manipulation of the signal data 24, 26 of an epoch 72 that willtend to self-neutralize. For example, certain resonance frequencies mayoccur at a frequency higher or lower than that of the background noise.Thus, shifting signal data 24, 26 slightly forward or backward within anepoch 72 and adding or multiplying the signal data 24, 26 together mayprovide enhancement of certain features 90, while minimizing othersrelative thereto.

As in the illustrated embodiment of FIG. 10, a weight table 118 containsa value of weight 122 for each feature segment 92 contained within afeature map 96. The weights 122 are arranged within the weight table 118according to the feature segments 92 to which they apply. That is, theweights become coefficients. For example, the weight 122 a contained inthe first row and first column of the weight table 118 corresponds tothe feature segment 92 a in the first row and first column of thefeature map 96. The weights 122 illustrated in FIG. 10 determinecontributions or emphasize certain feature segments 92 while minimizingor virtually eliminating the effect of others.

In certain embodiments, upon leaving a weighting module 116, featuresegments 92 may enter a consolidation module 124. In certainembodiments, the consolidation module 124 may apply 126 the weight table118 to the feature map 96. Additionally, a consolidation module 124 mayact to compile what was previously separated by the feature expansionmodule 84. For example, if an epoch 72 was expanded into featuresegments 92 in the feature expansion module 84, then the consolidationmodule 124 may collect the feature segments 92 in an effort to form afeature 90.

A consolidation module 124 may consolidate feature segments 92 by anysuitable method or mathematical manipulation. In certain embodiments,consolidation may include superposition 128 of the feature segments 92.This can be a weighted sum of values. If the feature operators 88 andweights 122 were effective, when the feature segments 92 are assembledback together (e.g. added, consolidated), a feature 90 (e.g. anon-random shape of a waveform) unique to the state of the epoch 72 (andnot existing when that state does not exist) may appear.

In the embodiments of FIGS. 11 and 12, example learning epochs 72 a, 72b corresponding to mutually exclusive states A and B are illustrated.Both epochs 72 a, 72 b may contain a signal 24 appearing to be random.After processing by one or more effective feature operators 88 andweights 122, a feature 90 corresponding to one state (A or B) and notthe other (B or A) may be generated. In certain embodiments,recognizable, non-random patterns 90 or features 90 may be generated inepochs 72 corresponding to both states. In such cases, the featureoperators 88 and weights 122 may still be considered effective so longas the feature 90 corresponding to state A is discernibly different fromthe feature 90 corresponding to state B.

In certain embodiments, before or after the feature segments 92 aresuperimposed 128, the consolidation module 124 may aggregate 130 thefeature segments 92 or the resulting feature 90. Aggregation 130 mayemploy any method or mathematical manipulation directed to reducing thefeature segments 92 or features 90 to a single numeric valuecharacterizing the epoch 72. In certain embodiments, aggregation 130 mayinvolve assigning a numerical value corresponding to the magnitude ofthe presence or non-presence of a particular feature 90.

In certain embodiments, after processing by a feature expansion module84, a weighting module 116, and a consolidation module 124, a typingconfidence module 132 may evaluate the ability of the various featureoperators 88, weights 122, or the like to generate or extract features90 that reliably segregate epochs 72 according to their state.Evaluation of the processing may be accomplished in any suitable manner.

In one embodiment, the assigned numerical values corresponding to eachepoch 72 may be plotted. A distribution 134 of epochs 72 correspondingto state A may be compared to a distribution 136 of epochs 72corresponding to state B. If desired, an optimal threshold value 138that best divides the two distributions 134, 136 may be selected. Thepercentage of epochs 72 corresponding to state A falling on the correctside of the threshold value 138 may be calculated. Similarly, thepercentage of epochs 72 corresponding to state B falling on the correctside of the threshold value 138 may be calculated. If the calculatedpercentages surpass a selected level of statistical significance, theprocessing may be considered effective. The learning system 76 maycontinue to iterate through various feature operators 88 and weights 122until an optimal procedure or formula for segregating epochs 72 by stateis determined.

In certain embodiments, the optimized procedure or formula forsegregating epochs 72 by state may be forwarded to a separation key 80.For example, as illustrated in FIG. 13, a separation key 80 may outlinethe feature operators 88 a, 88 b successfully and reliably applied togenerate a feature map 96. The separation key 80 may indicate theweights 122 successfully applied to the feature segments 92. Aseparation key 80 may also contain the optimal threshold value 138,superposition 130 procedure, aggregation 130 procedure, or the like thatwere found by the learning system 76 to be the most effective. Ingeneral, a separation key 80 contain anything learned by the learningsystem 76.

It may be noted that the portion of the separation key 80 illustrated inFIG. 13 has been found by an event contrast stacker 64 in accordancewith the present invention to be effective in segregating portions of asignal 24 pertaining to geological formations 19 containing sand fromportions of a signal 24 pertaining to geological formations 19containing little or no sand. That is, by expanding an epoch 68 into thetwelve noted frequency bands 100 and three noted time segments 98 andapplying the noted weights 122, a feature 90 corresponding to thepresence of absence of sand may be generated.

Referring to FIG. 14, a separation key 80 may be presented graphically,if desired. For example, a vertical axis 140 may represent the weighting122. A horizontal axis 142 may represent the frequency bands 100. Eachgraph 144 a, 144 b, 144 c may represent one of the various time segments98. A height 146 applied to a column 148 for each frequency band 100 mayequal the weight 122 to be applied to that frequency band 100 for thattime segment 98. FIG. 14 is arranged to be a graphical representation ofthe separation key 80 of FIG. 13.

Referring to FIG. 15, the learning system 76, once completed, mayforward the separation key 80 to the classification system 78. Theclassification system 78 may receive and process classification epochs74 in accordance with the procedures contained within the separation key80.

In typical embodiments, the classification epochs 74 may be differentfrom the learning epochs 72. Thus, the classification system 78 may testthe separation key 80 on epochs 74 that the event contrast stacker hasnever “evaluated” to provide a more rigorous tester validation. If thestate of each epoch is known, the process is a validation. If not,classification epochs 74 are prediction outputs for use. Additionally,the number of classification epochs 74 may be greater than the number oflearning epochs 72. Classification epochs 74 may or may not be providedwith a label 70 indicating the state of the geological formation 19 fromwhich they were collected. During evaluation of a separation key 80,labels 70 containing state information may be helpful in comparingactual state segregation against state segregation generated by theseparation key 80. Once a separation key 80 has been evaluated andproven reliable, any epoch 74 corresponding to geological formations 19of unknown state may be received and processed by the classificationsystem 78.

In certain embodiments, similar to a learning system 76, aclassification system 78 may contain a feature expansion module 84 and aconsolidation module 124. Unlike a learning system 76, however, aclassification system 78 need not iterate through various procedures tocollect the most effective feature operators 88, weights 122,superposition 128 procedures, aggregation 130 procedures, optimalthreshold value 138, or the like. A classification system 78 inaccordance with the present invention applies the feature operators 88,weights 122, superposition procedures 128, aggregation procedures 130,optimal threshold value 138, or the like that are provided in theseparation key 80.

Accordingly, unlike the feature expansion module 84 of the of thelearning system 76, the feature expansion module 84 of theclassification system 78 does not apply a multitude of feature operators88 to expand the epochs 74. The feature expansion module 84 of theclassification system 78 simply applies the effective feature operators88 delivered thereto as part of the separation key 80.

In selected embodiments, a weighting module 116 need not be included ina classification system 78. A consolidation module 124 of theclassification system 78 may apply the weight table 118 contained in theseparation key 80. Similarly, the consolidation module may apply thesuperposition procedure 128 and aggregation procedure 130 provided inthe separation key 80. Upon completion of processing by theconsolidation module 124, the resulting data may be passed to an outputgenerator 82 to be converted into useful and easily accessibleinformation.

Referring to FIGS. 16 and 17, in certain applications, after sufficientconfidence is developed in a particular separation key 80, it may not benecessary to enter the learning system 76 every time a new signal 24 isclassified. Thus, an event contrast stacker 64 may be formed without alearning system 76. In such embodiments, a proven separation key 80 maybe coded within the classification system 78.

For example, once a separation key 80 is generated for distinguishingbetween geological formations 19 containing oil above a desiredproduction level and geological formations with no oil or with oil belowa desired production level, a data bundle 152 contain signals 24 from ageological formation 19 having an unknown state may be analyzed. Ifdesired, the data bundle 152 may be processed before entering an eventcontrast stacker 64. In one embodiment, the data bundle 152 may beprocessed by a signal migrator 38. A record 153 of the data bundle 152may be generated. The record 153 may be forwarded to an event contraststacker 64 and be divided by a signal pre-processor 66 intoclassification epochs 74. Since the state of the epochs 74 is unknown,the epochs 74 cannot be labeled therewith. However, each classificationepoch 74 may be labeled with a coordinate (e.g. ordered triplet) orother designation indicating the location from which the epoch 74originated.

Upon processing by a classification system 78 having the internalseparation key 80, it may be determined whether the geological formation19 corresponds more to a geological formations 19 containing oil above adesired production level or not, that is a geological formation with nooil or with oil below a desired production level. Accordingly, a usermay determine which geological formations 19 are likely to produce oilas desired when tapped by a well.

As discussed hereinabove, certain embodiments of systems in accordancewith the present invention may incorporate an event contrast stacker 64into a single unit having a simple user interface. Such embodiments maybe supplied with an internal database 150 containing various separationkeys 80 for differentiating between hundreds or thousands of state pairslikely to be found in geological formations. A display and userinterface may provide to a user the ability to select which separationkey 80 is used. In an alternative embodiment, an event contrast stacker64 may query the database 150 to find a separation key 80 most suited toa particular state comparison selected by a user.

An internal database 150 containing multiple separation keys 80 may alsobe supplied in addition to a learning system 76. An event contraststacker 64 containing both an internal database 150 and a learningsystem 76 may more quickly analyze common states using separation keys80 recorded in the database 150, while still providing the hardware andsoftware to learn how to segregate additional states of geologicalformations 19. In selected embodiments, an event contrast stacker 64 inaccordance with the present invention, may store a copy of every newseparation key 80 generated in an internal database 150 for futurereference. In such a manner, the event contrast stacker 64 may quicklybuild up a database 150 of effective feature operators 88, weights 122,and so forth.

Referring to FIG. 18, an event contrast stacker 64 in accordance withthe present invention may present data in multiple useful formats. Thefollowing output formats are presented as exemplary models and are notto be interpreted as being restrictive of the available formats. Forexample, these formats may include activation value plots 154,reliability matrices 156, contrast stacked signals 158, contrast seismicvolume 160, and so forth. Additionally, several useful matrices may bederived from a reliability matrix 156. These derivatives may include adiscrimination accuracy matrix 162, similarity matrix 164, anddissimilarity matrix 166. Furthermore, in certain embodiments, it may bedesirable to simply output a plot of the feature 90 produced by theevent contrast stacker 64 before it is aggregated 130 to a numericalvalue.

Referring to FIG. 19, an activation value plot 154 may have a spacingaxis 168 and a magnitude axis 170. The spacing axis 168 may simply allowa plotted point 172 to be slightly spaced in the horizontal directionfrom neighboring plotted points 172. Thus, the plotted points 172 may bearranged to avoid entirely overlapping one another. The magnitude axis170 may have a range 176 selected to illustrate a magnitude of thepresence or non-presence of a particular distinguishing featurecontained in each classified epoch 74.

In certain embodiments, the spacing axis 168 may also be dividedaccording to geological formations 19. For example, a first section 174a of the spacing axis 168 may correspond to a first geological formation19 penetrated by a first well. A second section 174 b of the spacingaxis 168 may correspond to a second geological formation 19 penetratedby a second well, and so on. The presence of a well may provideinformation concerning the state of the geological formation. Forexample, in the illustrated embodiment, wells one through three may beknown gas producing wells 178, while wells four and five are known to bedry holes 180 or non-producing wells 180. Wells six and seven may beprospective wells 182 that are yet to be drilled.

To create an activation value plot 154, an assigned numerical value foreach classified epoch 74 may be scaled or otherwise manipulated to fitin the magnitude range 176 of the plot 154. In one embodiment of asystem in accordance with the present invention, the assigned numericalvalue is manipulated to fit within the range 176 from −1 to +1. Theoptimum threshold value 138 may be normalized to zero. Each small circle172 or plotted point 172 may represent an epoch 74 of highly processedsignal activity.

In the illustrated embodiment of FIG. 19, the producing wells 178exhibit mostly positive (from 0 to +1) spectrum activation values. Incontrast, the non-producing wells 180 exhibit mostly negative (from 0 to−1) spectrum activation values. The activation value plots 154 ofprospective wells 182 may be compared to activation value plots for theproducing and non-producing wells 178, 180. Well six shows a strongcorrelation to the producing wells 178. Thus, it may likely beprofitable to drill well six. On the other hand, well seven shows astrong correlation to the non-producing wells 189. Thus, it is likely tobe unprofitable to drill well seven.

The ability to non-invasively and accurately predict the state of ageological formation 19 may be profitable. Drilling a hydrocarbon wellcan be very expensive. By more accurately predicting which prospectivewells are likely to produce, large sums of money may be saved by notdrilling in unproductive sites.

Referring to FIG. 20, activation value plots 154 may be used to create a“fingerprint” corresponding to a particular state. Activation valueplots 154 illustrate the relative probability that an epoch 74corresponding to a particular state will have a particular magnitude.From the illustrated activation value plot 154, it can be seen that welleight has produced a dense concentration of plotted points 172 havingvalues between 0.5 and 1.0 on the magnitude axis 170. While the plottedpoints 172 of FIG. 20 are similar to those shown in FIG. 19 for theproducing wells 178, the point distribution 172 for the producing wells178 in FIG. 19 is more spread out.

Thus, by examining the finger print illustrated in an activation valueplot 154, a range of information may be extracted. For example, welleight is very different from the non-producing wells 180, but is notexactly like the producing wells 178. Further analysis may show thatwell eight is an exceptionally high producing gas well. Accordingly,variations in the activation value plots 154 may provide a spectrum ofinformation.

Referring to FIG. 21, in certain embodiments of a system in accordancewith the present invention, the classification accuracy of a particularseparation key 80 may be determined by creating a reliability matrix156. A reliability matrix 156 may be created by comparing theclassification of an epoch 74 as corresponding to a particular statewith the actual state associated with that epoch 74. For example, if aparticular epoch 74 was classified as a “state A” epoch, then one of twothings can be true. The epoch 74 can either correspond to a state A orstate B. The same may be true for an epoch 74 classified as state B.

After comparing classification data against actual data, four numbersmay be produced: the number 184 of state B epochs 74 erroneouslyclassified as a state A epochs 74; the number 186 of state A epochs 74correctly classified as state A epochs 74; the number 188 of state Aepochs 74 erroneously classified as a state B epochs 74; and the number190 of state B epochs 74 correctly classified as state B epochs 74. Bydividing these numbers (i.e., the numbers indicated by the identifiers184, 186, 188, 190) by the total number of actual epochs 74 related totheir predicted state, accuracy or reliability percentages 192 may becalculated.

Reliability percentages 192 may be incorporated into a reliabilitymatrix 156. For example, if 100 epochs 74 of state A where classifiedand 94 where correctly classified as corresponding to state A, then theAA (matrix notation) reliability percentage 192 a would be 94%. Thatwould leave 6 epochs 74 of state A that where erroneously classified asstate B. The AB reliability percentage 192 b would be 6%. The remainingBB and BA reliability percentages 192 c, 192 d may be calculated in asimilar manner.

A reliability matrix 156 may provide the user a better understanding ofthe extent to which a particular classification may be trusted. In theillustrated example, a user may be quite comfortable that, using thisparticular separation key 80, a epoch 74 of state A will indeed beclassified as a state A epoch 74 as the reliability matrix 156 indicatesthat 94% of all state A epochs 74 were correctly classified.

Referring to FIG. 22, multiple reliability matrices 156 a, 156 b, 156 c,. . . , 156 n may be used to generate a discrimination accuracy matrix162. Reliability matrices 156 provide the probability that two states (Aand B, B and C, C and D, or the like) will be classified correctly. Adiscrimination matrix 162, on the other hand, may provide informationabout how well a particular separation key 80 is able to differentiateseveral states.

For example, a particular reliability matrix 156 b may state that whencompared with state B, an event contrast stacker 64 may correctlyclassify 88% of all state A epochs 74. When compared with state A, thatsame event contrast stacker 64 may correctly classify 90% of all state Bepochs 74. A total classification accuracy 194 b of the event contraststacker 64 with respect to states A and B may be determined by averagingthe two correct reliability percentages 192. In the illustratedembodiment of FIG. 22, the generation of various total classificationaccuracies 194 is shown in a matrix notation 196 as well as a numericexample 198. In applications where the number of state A epochs 74analyzed does not equal the number of state B epochs 74 analyzed, atotal classification accuracy 194 may be determined by adjusting, suchas by dividing the total number of correct classifications (regardlessof state) by the total number of epochs 74 analyzed.

Once a total classification accuracy 194 has been generated for aparticular pair of states, this value 194 may be inserted in theappropriate locations of the discrimination accuracy matrix 162. It maybe noted that discrimination matrices 162 are symmetric, therebyreducing the number of calculations necessary to complete the matrix162. Reliability matrices 156 may be generated and total classificationaccuracies 194 calculated using selected state pairs until thediscrimination matrix 162 is complete.

A complete discrimination matrix 162 may provide the user with acomparison of the similarities of a variety of states. It may be notedthat the diagonal 200 of the discrimination matrix 162 may often containvalues near 50%. The diagonal 200 contains total classificationaccuracies 194 of a particular state compared against itself. As wouldbe expected, an event contrast stacker 64 may not repeatably distinguisha given state from itself. Therefore, it is typically right half thetime and wrong half the time.

Referring to FIG. 23, a discrimination matrix 162 may be converted to adissimilarity matrix 166 by a dissimilarity transformation 202.Dissimilarity matrices 166 provide a method for comparing how differenta particular state is from another state. As can be seen, the diagonal200 contains low dissimilarity values. This is to be expected as stateshave a low (theoretically zero) dissimilarity with themselves.

Referring to FIG. 24, a discrimination matrix 162 may be converted to asimilarity matrix 164 by a similarity transformation 204. As can beseen, the diagonal 200 contains high similarity values. This is to beexpected as states are similar to themselves. Similarity matrices 164may be particularly useful. A similarity matrix 164 enables a user toobjectively calculate how similar a particular state is to anotherstate. This comparison may have a profound impact on the ability of auser to predict and quantify states.

Referring to FIG. 25, a similarity matrix 164 may be presented as a bargraph. The bar graph provides a visual representation of areas ofsimilarity and dissimilarity between various geological formations 19having various states. For example, in the illustrated embodiment, twogeological formations 19 known to contain oil, two geological formations19 known to contain gas, and three geological formations 19 havingprospective well sites are compared. As expected, the geologicalformations 19 containing oil show a high similarity to one another.Similarly, the geological formations 19 containing gas show a highsimilarity to one another. The first prospective well shows nosimilarity to oil or gas. The second prospective well shows a similarityto oil. The third prospective well shows similarity to gas.

Referring to FIG. 26, an output generator 82 of an event contraststacker 64 in accordance with the present invention may outputinformation in the form of a contrast stacked signal 158 or contraststacked trace 158. As discussed hereinabove, in certain embodiments, anevent contrast stacker 64 may receive a complex and apparently randomsignal 24. The event contrast stacker 64 may divided the signal 24 intoepochs 68. Each epoch 68 may be processed by the event contrast stacker64 in an effort to reveal features 90 (inherent characteristicsindicating non-random information encoded within the signal 24) thatcorrespond to a particular state of the formation 19 from which thesignal 24 was collected.

Once the features 90 of each epoch 68 have been expanded, the epochs 68may be reassembled in the order they were taken from the signal 24. Thisreassembly may result in the formation of a contrast stacked signal 158.That is, a signal 158 that is stacked, collected, summed, or otherwiseprocessed in a manner to draw out contrasts between portions 206 of thesignal 158 pertaining to state A and portions 208 of the signal 158pertaining to state B.

Referring to FIGS. 27-30, if desired, a collection of contrast stackedsignals 158 may be arranged in their proper relative locations inthree-dimensional Euclidean space. A collection of properly positionedcontrast stacked signals 158 may constitute a contrast seismic volume160. In certain embodiments, a contrast seismic volume 160 may representa map of a selected physical volume of a geological formation 19. Thecontrast seismic volume 160 may indicate locations corresponding todifferent states.

Various methods may be used to operate on a contrast seismic volume 160and generate three dimension images 210 or two dimensional images 212 ofa geological formation 19. In selected embodiments, three dimensionalimages 210 may be generated by interpolating between the collection ofcontrast stacked signals 158. The illustrated embodiment of FIG. 28provides a three dimension image 210 of volumes 214 corresponding tostate A and volumes 216 corresponding to state B. In certainembodiments, volumes 214 corresponding to state A may represent an oildeposit that will produce oil above a selected threshold rate, whilevolumes 216 corresponding to state B may be formations that will notproduce oil at a rate above a selected threshold value.

As stated hereinabove, a contrast seismic volume 160 may be used togenerate two dimensional images 212. In the illustrated embodiment ofFIG. 29, a horizontal, two dimensional slice 212 provides views ofregions 218 corresponding to state A and regions 220 corresponding tostate B at a certain physical depth. In the illustrated embodiment ofFIG. 30, a vertical, two dimensional slice 212 provides additional viewsof regions 218 corresponding to state A and regions 220 corresponding tostate B at a certain distance.

Referring to FIGS. 31 and 32, in certain embodiments, it may bedesirable to divide a contrast seismic volume 160 into varioussub-volumes 222. The size or number of the sub-volumes 222 may varyaccording to the desired resolution. In selected embodiments, eachsub-volume 222 may be labeled with a number 224 indicating thecorrespondence of that sub-volume 222 to a particular state. Theresulting collection of numbered sub-volumes 222 may form a numeric plot226.

The number 224 may be selected by quantifying the presence or absence ofa feature 90 in a portion 206, 208 of the contrast stacked signal 158contained within the sub-volume 222. If desired, the number 224 may bethe numerical value assigned the portion 206, 208 in the aggregation 130process. In selected embodiments, more than one contrast stacked signal158 may pass through a sub-volume 222. In such situations, the number224 may be selected to represent the presence or absence of a feature 90in selected portions 206, 208 of the various contrast stacked signals158 contained within the sub-volume 222. In one embodiment, the number224 may be an average of the numerical value assigned to the selectedportions 206, 208 in the aggregation 130 process.

In selected embodiments, each sub-volume 222 may have a color 228applied thereto. The color 228 may provide a visual key indicating thecorrespondence of that sub-volume 222 to a particular state of interest.The resulting collection of colored sub-volumes 222 may be combined toform a color plot 230. For example, in one embodiment, the variouscolors 228 applied to the sub-volumes 222 may represent a spectrum orscale of color. Sub-volumes 222 containing portions 206, 208 of thecontrast stacked signal 158 representing a high probability of thedesired state, represented by the high incidence of a particular feature90, may be assigned a color 228 at one end of the selected colorspectrum. Conversely, sub-volumes 222 containing portions 206, 208 ofthe contrast stacked signal 158 representing a low incidence of theparticular feature 90 may be assigned a color 228 at the other end ofthe color spectrum or other contrasting color. Sub-volumes 222containing portions 206, 208 of the contrast stacked signal 158representing an intermediate incidence of the particular feature 90 maybe assigned a corresponding color 228 from the interior of the colorspectrum.

Various colors 228 or color spectra may be applied to any outputgenerated by an output generator 82 in accordance with the presentinvention. For example, in addition to the color plots 230 describedhereinabove, colors 228 and color spectra may be applied to activationvalue plots 154, reliability matrices 156, contrast stacked signals 158,contrast seismic volumes 160, three dimensional images 210, twodimensional images 212, or the like. Colors 228 and color spectra may beused to immediately communicate information to a viewer regarding thedegrees of presence or absence of a particular state within a geologicalformation 19.

In selected embodiments, traces 24 that are migrated may be combinedwith color coded contrast stacker signals 158. The end result may be acombination of information available in seismic traces 24 beforeprocessing by an event contrast stacker 64 and information obtainedafter processing by an event contrast stacker 64. The color codedcontrast stacked signals 158 may enhance the seismic traces 24 andindicate what locations in the seismic traces 24 represent a particularstate of the geological formation.

In certain embodiments, activation value plots 154, reliability matrices156, contrast stacked signals 158, contrast seismic volumes 160, threedimensional images 210, two dimensional images 212, numeric plots 226,color plots 230, or the like may be used to quantify the portions 206,208, volumes 214, 216, and regions 218, 220 corresponding to differentstates. For example, if state A represents the presence of an oildeposit in a geological formation 19, a three dimensional image 219 mayprovide the ability to quantifying the number of barrels of oil that maybe contained in a volume 214 corresponding to state A.

Additionally, signals 24 may be collected from a particular geologicalformation 19 at different times. By using the methods and structures inaccordance with the present invention, the portions 206, 208, volumes214, 216, and regions 218, 220 pertaining to a particular state may becalculated for each time the signals 24 are collected. The portions 206,208, volumes 214, 216, and regions 218, 220 may be compared betweendifferent collections of signals 24 to determine how the geologicalformation 19 is changing. For example, the volume 214 corresponding tothe presence of an oil deposit may be quantified in a first year. Insubsequent years, such as after each year of pumping, signals 24 mayagain be collected and the volume 214 corresponding to the presence ofan oil deposit may again be quantified. By comparing the quantities, theimpact of pumping on the oil deposit may be evaluated.

Referring to FIGS. 33-36, in certain embodiments, an event contraststacker 64 may migrate or to assist in migrating seismic traces 24. Asdiscussed hereinabove, migration is an attempt to locate the source ofsignals 24 that have traveled large distances (e.g. long times). One ofthe techniques that may be used to migrate seismic traces is eventaligning. An event 232 may be defined as a section of a seismic trace 24corresponding to the reflected wave 22 caused by a particular reflector20. A seismic trace 24 is, in reality, a collection of events 232represented by a shape of a waveform, obscured by noise.

In certain applications, selected traces 24 a, 24 b, 24 c may containcommon events 232. Common events 232 may be defined as reflected waves22 originating from a common reflector 20. By locating waveshapes orrecognizable common events 232 in multiple traces 24 a, 24 b, 24 c, thetraces 24 may be adjusted until common events 232 are aligned, such asin time. Aligning may facilitate piecing together the various traces 24to form a collection of fully migrated traces 24.

An event contrast stacker 64 in accordance with the present inventionmay be used to compare signals 24 and identify signals 24 a having aninformation content that is either more readily exposed or simplystronger than others. Signals 24 a having such high information contentand visibility may then be used for facilitating processing of othersignals 24 b, 24 c. For example, a plurality of events 232 b, 232 c maybe identified along a signal 24 a of high information visibility. Otherlow information signals 24 b, 24 c may contain one of the plurality ofevents 232. It may be difficult to align two low information signals 24b, 24 c if a common event 232 is not readily located. However, the highinformation signal 24 a may contain an event 232 b in common with a lowinformation signal 24 b as well as an event 232 c from another lowinformation signal 24 c. Thus, using the high visibility or simply highinformation signal 24 a, the low information signals 24 b, 24 c may bealigned with respect to one another.

The following examples will illustrate the invention in further detail.It will be readily understood that the present descriptions of certainaspects of the invention, as generally described and illustrated in theExamples herein, are merely exemplary of embodiments of apparatus andmethods in accordance with the present invention. Thus, the followingmore detailed description of certain embodiments of methods andformulations in accordance with the present invention, as represented inExamples I through IV, is not intended to limit the scope of theinvention, as claimed, but is merely representative of possibleembodiments and applications of the present invention.

EXAMPLE I

Referring to FIGS. 37-43, in the present example, signals 24 (post-stackgathers) were provided from a twenty square mile area of an operatingoil field. The signals had previously been used to generate conventionalseismic volumes illustrated in FIGS. 37 and 39. Twelve wells 30 weredrilled based on the seismic volumes. As can be seen, all the wells 30are positioned in areas 234 that the seismic volumes indicated arelikely locations for oil.

Of the twelve bores 30 or wells 30, two resulted in oil wells 30 a, 30b, two resulted in dry holes 30 c, 30 d, and two resulted in wet holes30 e, 30 f (water filled). The states of the remaining six wells 30 g,30 h, 30 i, 30 j, 30 k, 30 m were known to the owners of the oil field,but were withheld until processing in accordance with the presentinvention was completed.

As illustrated in FIG. 41, selected signals 24 corresponding to each ofthe wells 30 were provided for processing. The number 236 of signals 24provided for each well 30 ranged from 154 to 177. The signals 24 wereprocessed by an event contrast stacker 64 in accordance with the presentinvention.

The area of interest, or pay horizon 62, of the oil field of the presentexample was located about 1.1 seconds from the surface 16. As a result,an epoch 68 was taken from each of the signals 24 in the range extendingfrom 0.1 seconds before the pay horizon 62 to 0.1 seconds after the payhorizon 62. Thus, each epoch 68 represented 0.2 seconds (200milliseconds) of a signal 24. Since the pay horizon 62 of the actual oilfield did not remain at a constant depth, the exact location of thevarious epochs 68 varied for different wells 30. For example, the epochs68 corresponding to well one 30 a extended from time 0.976 to time 1.176while the epochs 68 from well two 30 b extended from time 0.964 to time1.194.

The geological formation 19 containing well two 30 b, an oil well, wasconsidered an example of state A (i.e. an oil producing location). Thegeological formations 19 containing well three 30 c, a dry hole, andwell five 30 e, a wet hole, were considered examples of state B (i.e.non oil producing locations). Epochs 68 corresponding to wells two 30 b,three 30 c, and five 30 e were used as learning epochs 72 and processedby a learning system 76 in accordance with the present invention. Epochs68 corresponding to wells one 30 a, four 30 d, and six 30 f throughtwelve 30 m were used as classification epochs 74 and processed by aclassification system 78 in accordance with the present invention.

After processing the learning epochs 72, the learning system 76 produceda separation key 80 illustrated in part by FIG. 42. It was determinedthat each epoch 72 may be weighted in time space with a Gaussiandistribution 102 centered at time 100 milliseconds (halfway though theepoch 72) with a time width 106 of 100 milliseconds. It was alsodetermined that each epoch 72 may be divided in a frequency space intofive frequency bands 100. The frequency bands 100 may be weighted withGaussian distributions 102 centered at 25 Hz, 50 Hz, 75 Hz, 100 Hz, and120 Hz, all with frequency widths 110 of 5 Hz. Weights 122 for theresulting feature segments 92 may be applied as illustrated.

The learning epochs 72 and the classification epochs 74 were processedby the classification system 78 using the separation key 80 developed bythe learning system 76. Each epoch 68 was expanded into feature segments92. The feature segments 92 corresponding to a particular epoch 68 wereweighted, superimposed 128, and aggregated 130 to a numerical value. Thenumerical values were normalized and plotted in the activation valueplot 154 of FIG. 43. Each processed epoch 68 is represented by a plottedpoint 172. Plotted points 172 between 0.0 and 1.0 indicate acorrespondence to an oil producing state. Plotted points 172 between 0.0and −1.0 indicate a correspondence to a non oil producing state.

As seen in FIG. 43, wells one 30 a and two 30 b were properly classifiedas oil wells. Wells three 30 c though six 30 f were properly classifiedas non-producing wells. Of the unknown test wells (i.e. wells seven 30 gthough twelve 30 m), wells eight 30 h and eleven 30 k were classified asoil wells, while wells seven 30 g, nine 30 i, ten 30 j, and twelve 30 mwere classified as non-producing wells. Upon viewing the data, the oilfield owners confirmed that wells eight 30 h and eleven 30 k were indeedoil wells and wells seven 30 g, nine 30 i, ten 30 j, and twelve 30 mwere indeed non producing wells. Thus, the processing of the eventcontrast stacker 64 in accordance with the present invention wasvalidated.

A horizontal, two dimensional slice 212 of the oil field as processed inaccordance with the present invention is illustrated in FIG. 38. Avertical, two-dimensional slice 212 of the oil field as processed inaccordance with the present invention is illustrated in FIG. 40. As canbe seen in FIGS. 38 and 40, all the oil wells 30 a, 30 b, 30 h, 30 k arepositioned in areas 238 that an event contrast stacker 64 in accordancewith the present invention predicted to contain oil. All of the dry andwet holes 30 c, 30 d, 30 e, 30 f, 30 g, 30 i, 30 j, 30 m are positionedin areas 240 that an event contrast stacker 64 in accordance with thepresent invention predicted not to contain oil. Additionally, otherareas 242 are illustrated to indicate where future wells 30 may bedrilled with a high likelihood of finding extractable oil.

EXAMPLE II

Referring to FIGS. 44-49, in the present example, traces (post-stackgathers) were collected from an eight square mile area of an operatinggas field. The data was used to form the conventional seismic volumeillustrated in FIG. 47. Five wells 30 were drilled based on that seismicvolume. As can be seen, all the wells 30 are positioned in areas 234that the seismic volume indicated as likely locations for gas.

Of the five wells 30, well one 30 a resulted in a gas well and well two30 b resulted in non-producing wet hole (producing water not gas). Thestates of the remaining three wells 30 c, 39 d, 30 e were known to theowners of the gas field, but were withheld until completion ofprocessing in accordance with the present invention.

As illustrated in FIG. 44, selected signals 24 corresponding to each ofthe wells 30 were provided for processing. The number 236 of signals 24provided for each well 30 ranged from 431 to 606. The signals 24 wereprocessed by an event contrast stacker 64 in accordance with the presentinvention.

The area of interest, or pay horizon 62, of the gas field of the presentexample was located about 0.85 seconds from the surface 16. In a firstapplication of an event contrast stacker 64 in accordance with thepresent invention, an epoch 68 was taken from each of the signals 24 inthe range extending from 0.1 seconds before the pay horizon 62 to 0.1seconds after the pay horizon 62. Thus, each epoch 68 of the firstapplication represented 0.2 seconds (200 milliseconds) of a signal 24.Thus, the epochs 68 corresponding to the wells 30 extended fromapproximately time 0.75 to time 0.95.

In a second application of an event contrast stacker 64 in accordancewith the present invention, an epoch 68 was taken from each of thesignals 24 in the range extending from 0.04 seconds before the payhorizon 62 to 0.04 seconds after the pay horizon 62. Thus, each epoch 68of the first application represented 0.080 seconds (80 milliseconds) ofa signal 24. Thus, the epochs 68 corresponding to the wells 30 extendedfrom approximately time 0.81 to time 0.89.

The geological formation 19 containing well one 30 a, a gas well, wasconsidered an example of state A (i.e. a gas-producing location). Thegeological formation 19 containing well two 30 b, a wet hole, wasconsidered an example of state B (i.e. a non-gas-producing location).Epochs 68 corresponding to wells one 30 a and two 30 b were used aslearning epochs 72 and processed by a learning system 76 in accordancewith the present invention. Epochs 68 corresponding to wells three 30 c,four 30 d, and five 30 e were used as classification epochs 74 andprocessed by a classification system 78 in accordance with the presentinvention.

After processing the learning epochs 72 corresponding to the 200millisecond time window, the learning system 76 produced a separationkey 80 a illustrated in part by FIG. 45. It was determined that eachepoch 72 may be weighted in time space with a Gaussian distribution 102centered at a time of 40 milliseconds (halfway though the epoch 72) witha time width 106 of 40 milliseconds. It was also determined that eachepoch 72 may be divided in frequency space into five frequency bands100. The frequency bands 100 may be weighted with a Gaussiandistribution 102 s centered at 25 Hz, 50 Hz, 75 Hz, 100 Hz, and 120 Hz,all with frequency widths 110 of 5 Hz. Weights 122 for the resultingfeature segments 92 may be applied as illustrated.

After processing the learning epochs 72 corresponding to the 80millisecond time window, the learning system 76 produced a separationkey 80 b illustrated in part by FIG. 46. It was determined that eachepoch 72 may be weighted in time space with a Gaussian distribution 102centered at time 100 milliseconds (halfway though the epoch 72) with atime width 106 of 100 milliseconds. It was also determined that eachepoch 72 may be divided in frequency space into three frequency bands100. The frequency bands 100 may be weighted with Gaussian distributions102 centered at 9.09 Hz, 18.18 Hz, and 27.27 Hz, all with frequencywidths 110 of 15 Hz. Weights 122 for the resulting feature segments 92may be applied as illustrated.

The learning epochs 72 and the classification epochs 74 corresponding tothe first and second applications were processed by the classificationsystem 78 using the respective separation keys 80 a, 80 b developed bythe learning system 76. For both applications, each epoch 68 wasexpanded into feature segments 92. The feature segments 92 correspondingto a particular epoch 68 were weighted, superimposed 128, and aggregated130 to a numerical value. From the resulting data, an output generator82 in accordance with the present invention generated respectivevertical, two-dimensional slices 212 a, 212 b.

As seen in FIGS. 47 and 48, in both the first and second applications,well one 30 a was properly positioned in an area 238 classified as gasproducing and well two 30 b was properly positioned in an area 240classified as non gas producing. Additionally, other presently untappedareas 242 were classified as likely to be gas producing. Of the unknown,test wells (i.e. wells three 30 c, four 30 d, and five 30 e), wells four30 d and five 30 e were classified as gas wells, while well three 30 cwas classified as a non-producing well. Upon viewing the data, the gasfield owners confirmed that wells four 30 d and five 30 e were indeedgas wells and well three 30 c was indeed a non-producing well. Thus, theprocessing of the event contrast stacker 64 in accordance with thepresent invention was validated.

EXAMPLE III

Referring to FIG. 50, in the present example, signal 24 was providedfrom a first geological formation 19 having gas production rates above aselected economic value and a geological formation 19 having gasproduction rates below a selected economic value. Epochs 68corresponding to the first and second geological formations 19 were usedas learning epochs 72 and processed by a learning system 76 inaccordance with the present invention.

After processing, the learning system 76 produced a separation key 80effective to separate geological formations 19 having gas productionabove a selected value from geological formations 19 having gasproduction below a selected value. The separation key 80, illustrated inpart in FIG. 50, instructs that each epoch 72 be divided in both timeand frequency space. In time space, each epoch 72 may be divided intofour time segments 98. The time segments 98 may be weighted withGaussian distributions 102 centered at 25 ms, 50 ms, 75 ms, and 50 ms.The time segments may have time widths 106 of 6 ms, 6 ms, 6 ms, and 50ms, respectively.

In frequency space, each epoch 72 may be divided into six frequencybands 100. The frequency bands 100 may be weighted with Gaussiandistributions 102 centered at 20.83 Hz, 41.67 Hz, and 83.33 Hz, all withfrequency widths 110 of 10 Hz, and at 20.83 Hz, 41.67 Hz, and 83.33 Hz,all with frequency widths 110 of 5 Hz. Weights 122 for the resultingfeature segments 92 may be applied as illustrated.

EXAMPLE IV

Referring to FIG. 51, in the present example, signal 24 was providedfrom a first collection of geological formations 19 producinghydrocarbons (i.e. gas, oil, or gas and oil) and a second collection ofgeological formations 19 producing water. Epochs 68 corresponding to thefirst and second collections were used as learning epochs 72 andprocessed by a learning system 76 in accordance with the presentinvention.

After processing, the learning system 76 produced a separation key 80effective to separate geological formations 19 producing a hydrocarbonfrom geological formations 19 producing water. The separation key 80,illustrated in part in FIG. 51, instructs that each epoch 72 be dividedin both time and frequency space. In time space, each epoch 72 may bedivided into three time segments 98. The time segments 98 may beweighted with Gaussian distributions 102 centered at 50 ms, 100 ms, and150 ms, all with time widths 106 of 100 ms. In frequency space, eachepoch 72 may be divided into three frequency bands 100. The frequencybands 100 may be weighted with Gaussian distributions 102 centered at 10Hz, 20 Hz, and 30 Hz, all with frequency widths 110 of 10 Hz. Weights122 for the resulting feature segments 92 may be applied as illustrated.

From the above discussion, it will be appreciated that the presentinvention provides an integrated waveform analysis method and apparatuscapable of extracting useful information from highly complex, irregular,and seemingly random or simply noise-type waveforms such as seismictraces and well log data. Unlike prior art devices, the presentinvention provides novel systems and methods for signal processing,pattern recognition, and data interpretation by means of observing andcorrelating the affects of a particular state on a geological formation.

The present invention may be embodied in other specific forms withoutdeparting from its essential characteristics. The described embodimentsare to be considered in all respects only as illustrative, and notrestrictive. The scope of the invention is, therefore, indicated by theappended claims, rather than by the foregoing description. All changeswithin the meaning and range of equivalency of the claims are to beembraced within their scope.

1. A method comprising: providing a first signal corresponding to afirst geological formation tapped by a hydrocarbon well producinghydrocarbons above a threshold rate; providing a second signalcorresponding to a second geological formation tapped by a hydrocarbonwell producing hydrocarbons below the threshold rate; expanding thefirst and second signal segments in at least one of frequency space andtime space by applying at least one feature operator to the first andsecond signal segments to generate a plurality of first feature segmentscorresponding to the first signal segment and a plurality of secondfeature segments corresponding to the second signal segment; weightingthe pluralities of first and second feature segments by applying aweight table to the pluralities of first and second feature segments togenerate a weighted plurality of first feature segments and a weightedplurality of second feature segments, respectively; superimposing theweighted plurality of first feature segments to form a first featurecomprising a non-random first pattern and the weighted plurality ofsecond feature segments to form a second feature having a second patterndistinct from the non-random first pattern; and generating a separationkey listing the at least one feature operator used to expand the firstand second signal segments and the weighting table applied to thepluralities of first and second feature segments.
 2. The method of claim1, wherein the first signal is a pre-stack, migrated seismic trace. 3.The method of claim 2, wherein the second seismic trace is a pre-stack,migrated seismic trace.
 4. The method of claim 1, wherein the firstseismic trace is a post-stack, migrated seismic trace.
 5. The method ofclaim 4, wherein the second seismic trace is a post-stack, migratedseismic trace.
 6. The method of claim 1, wherein the hydrocarbon well isan oil well.
 7. The method of claim 1, wherein the hydrocarbon well is agas well.
 8. The method of claim 1, wherein expanding comprises dividingthe first and second signal segments into selected frequency bands andwherein a first feature operator of the at least one feature operatorprovides selected central frequencies and corresponding frequency widthsto define the boundaries of the selected frequency bands.
 9. The methodof claim 8, wherein the first feature operator imposes a Gaussianweighting centered at each of the selected central frequencies to definethe selected frequencies bands.
 10. The method of claim 8, whereinexpanding comprises dividing the first and second signal segments intoselected time segments and wherein a second feature operator of the atleast one feature operator provides selected central times andcorresponding times widths to define the boundaries of the selected timesegments.
 11. The method of claim 10, wherein the second featureoperator imposes a Gaussian weighting centered at each of the selectedcentral times to define the selected time segments.
 12. The method ofclaim 8, wherein superimposing comprises collapsing the weightedplurality of first feature segments to form a first feature comprising anon-random first pattern and the weighted plurality of second featuresegments to form a second feature having a second pattern distinct fromthe non-random first pattern.
 13. The method of claim 12, furthercomprising providing a third signal corresponding to a third geologicalformation containing a prospective hydrocarbon well location.
 14. Themethod of claim 13, further comprising processing third signal inaccordance with the at least one feature operator and weighting tablelisted in the separation key to produce a third feature.
 15. The methodof claim 14, further comprising classifying the prospective hydrocarbonwell location as one of hydrocarbon well producing above the thresholdrate and a hydrocarbon well producing below the threshold rate based onthe correspondence of the third feature to one of the first feature andthe second feature.
 16. A method for predicting the state of ageological formation, the method comprising: providing a separation keyeffective to extract a first feature from a signal corresponding to afirst state and a second feature, distinct from the first feature, froma signal corresponding to a second state, the separation key listing atleast one feature operator and a weighting table; providing a first testsignal collected from the geological formation; applying the at leastone feature operator to expand the first test signal in at least one offrequency space and time space to generate a plurality of featuresegments; generating a weighted plurality of feature segments byapplying the weighting table to the plurality of feature segments;collapsing the weighted plurality of feature segments to generate athird feature; and classifying the geological formation as having one ofthe first state and second state based on the correspondence of thethird feature to one of the first feature and second feature.
 17. Themethod of claim 16, further comprising providing a second test signalcollected from the geological formation at a time after the first testsignal was collected.
 18. The method of claim 17, further comprisingprocessing the second test signal by applying the at least one featureoperator, applying the weight table, and collapsing to generate a forthfeature.
 19. The method of claim 18, further comprising quantifyingchanges in the geological formation between the time the first testsignal was collected and the time the second test signal was collectedby comparing the fourth feature to the first feature.
 20. The method ofclaim 16, further comprising selecting a color spectrum comprising atleast two colors and having a first extreme and a second extreme,opposite the first extreme, and assigning the color of the first extremeto the first feature and the color of the second extreme to the secondfeature.
 21. The method of claim 20, further comprising assigning thethird feature a color selected from the color spectrum based on therelative correspondence of the third feature to the first and secondfeatures.
 22. The method of claim 21, further comprising providing aplurality of test signals, each collected from a different location onthe geological formation.
 23. The method of claim 22, further comprisingprocessing the plurality of test signals by applying the at least onefeature operator, applying the weight table, and collapsing to generatea plurality of features.
 24. The method of claim 23, further comprisingassigning each of the plurality of features a color selected from thecolor spectrum based on the relative correspondence thereof to the firstand second features.
 25. The method of claim 24, further comprisinggenerating a two-dimensional image of the geological formation bycoloring portions of the image in accordance with the color assigned tothe feature of the plurality of features corresponding to the portions.26. A method for predicting the presence of extractable hydrocarbons ina geological formation, the method comprising: providing a separationkey effective to extract a first feature from signal collected from afirst geological formation tapped by a hydrocarbon well producing abovea threshold rate and a second feature from signal collected from asecond geological formation tapped by a hydrocarbon well producing belowthe threshold rate, the separation key listing at least one featureoperator and a weighting table; providing a test signal collected from athird geological formation; applying the at least one feature operatorto expand the test signal in at least one of frequency space and timespace to generate a plurality of feature segments; generating a weightedplurality of feature segments by applying the weighting table to theplurality of feature segments; superimposing the weighted plurality offeature segments to generate a third feature; and classifying the thirdgeological formation as one of producing hydrocarbons above thethreshold rate and producing hydrocarbons below the threshold rate basedon the correspondence of the third feature to one of the first featureand second feature.